Mistral Small 4 119B
Mistral Small 4 is a powerful hybrid model that acts as both a general instruction model and a reasoning model. It unifies the capabilities of three previously separate model families - Instruct, Reasoning (previously Magistral), and Devstral (coding) - into a single, unified model. With multimodal input, an efficient architecture, and flexible mode switching, it is a strong general-purpose model for a wide range of tasks.
Compared to the previous generation, a latency-optimized setup achieves roughly a 40 percent reduction in end-to-end completion time, and a throughput-optimized setup handles about 3x more requests per second than Mistral Small 3.
Key Features
- Hybrid instruct and reasoning - toggle between fast instant-reply mode and a reasoning mode that boosts performance with test-time compute when requested, with reasoning effort configurable per request.
- Vision - accepts both text and image input and analyzes visual content alongside text, producing text output.
- Mixture-of-experts architecture - 128 experts with 4 active per token, activating only a small fraction of the total parameters for each token.
- Long context - supports a 256k-token context window.
- Agentic - strong function-calling and JSON-output capabilities for tool use and automation.
- Multilingual - supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Chinese, Japanese, Korean, and Arabic.
- Strong system-prompt adherence - reliable behavior when steered by system prompts.
Use Cases
- Developers building coding and agentic workflows for software-engineering automation and codebase exploration.
- Enterprises deploying general chat assistants, agents, and document understanding.
- Researchers leveraging its math and reasoning capabilities.
Benchmarks
According to Mistral's internal evaluations, Mistral Small 4 in reasoning mode achieves competitive scores while generating significantly shorter outputs than comparable open models. On the AA LCR benchmark it reaches a score of 0.72 using roughly 1.6K characters of output, where other open models require several times more output for comparable performance. On LiveCodeBench it outperforms a leading 120B open model while producing about 20 percent less output, reflecting its focus on both quality and speed.
Full model details are available on the Hugging Face model card.
